A Deep Convolutional Neural Network for Lung Cancer Diagnostic
This work addresses lung cancer diagnosis for medical imaging, but it appears incremental as it builds on existing deep learning methods for a specific dataset.
The paper tackles lung cancer diagnosis from medical images by proposing a new deep convolutional neural network architecture to learn discriminant compact features, achieving high classification accuracy with low variance on the Kaggle Data Science Bowl 2017 dataset.
In this paper, we examine the strength of deep learning technique for diagnosing lung cancer on medical image analysis problem. Convolutional neural networks (CNNs) models become popular among the pattern recognition and computer vision research area because of their promising outcome on generating high-level image representations. We propose a new deep learning architecture for learning high-level image representation to achieve high classification accuracy with low variance in medical image binary classification tasks. We aim to learn discriminant compact features at beginning of our deep convolutional neural network. We evaluate our model on Kaggle Data Science Bowl 2017 (KDSB17) data set, and compare it with some related works proposed in the Kaggle competition.